The present embodiments relate to segmentation and machine learning of the segmentation, such as segmenting the left ventricle in magnetic resonance (MR) imaging.
Spatiotemporal images are routinely acquired for dynamic quantification of organ function. For example, MR imaging routinely acquires a stack of short-axis cardiac slices to image the entire left ventricle over time. Such images are used to assess an organ across multiple time points, at various locations. Segmentation of the left ventricle is used for computing several clinical indices of cardiovascular disease, such as ventricular volume, ejection fraction, left ventricular mass and wall thickness, as well as analyses of the wall motion abnormalities.
Current standard clinical practice for left ventricle segmentation is manual delineation, which is tedious, labor intensive, and prone to intra and inter observer variability. In one approach for automated segmentation, machine training is applied based on manually defined features. The performance depends on the high-level features. In another approach, the deep machine training learns task-specific image features through the annotation of large representative datasets in a fully automated fashion. For example, a fully convolutional segmentation model does not need explicit construction of task specific features. U-Net is a convolutional neural network with an architecture having a encoding path to capture context and a symmetric decoding path that enables end-to-end learning from fewer images. Both approaches for automated left ventricle segmentation involve many challenges: high class imbalance; inhomogeneity in intensity; considerable variability in the shape of the heart chambers across patients; variability in the shape across different views; and variability across different pathological cases. Greater accuracy in segmentation is desired.